Events - 2/36 - Databricks

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Unified Analytics – Unifying Data Pipelines & Machine Learning with Apache Spark

Regional Event

Detroit, Michigan

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll learn how to use ML frameworks (i.e. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment and manage the deployment of models to production.

Unified Analytics – Unifying Data Pipelines & Machine Learning with Apache Spark

Regional Event

Malvern, PA

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™️, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll learn how to use ML frameworks (i.e. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment and manage the deployment of models to production.

AWS + Databricks Dev Day Workshop | London

Regional Event

Europe

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™️, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

Building Reliable Data Lakes with Delta Lake | Hands-on Lab (Afternoon Session)

Partner Event

New York City

Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing. Delta Lake runs on top of your existing data lake, such as on Azure Data Lake Storage, AWS S3, Hadoop HDFS, or on-premise, and is fully compatible with Apache Spark APIs.

Join this hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.

Building Reliable Data Lakes with Delta Lake | Hands-on Lab (Morning Session)

Regional Event

New York City

Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing. Delta Lake runs on top of your existing data lake, such as on Azure Data Lake Storage, AWS S3, Hadoop HDFS, or on-premise, and is fully compatible with Apache Spark APIs.

Join this hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.

AWS + Databricks Dev Day Workshop | Seattle

Regional Event

Seattle, WA

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

Big Data Breakfast: Geospatial Analytics and AI in Public Sector

Regional Event

McLean, VA

Join our technical breakfast seminar to network with your peers and learn how to easily overcome the challenges of processing and analyzing large geospatial datasets in the cloud with a unified approach to data and analytics.

Building Reliable Data Lakes with Delta Lake | Hands-on Lab | Los Angeles

Regional Event

Los Angeles, CA

Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing. Delta Lake runs on top of your existing data lake, such as on Azure Data Lake Storage, AWS S3, Hadoop HDFS, or on-premise, and is fully compatible with Apache Spark APIs.

Join this hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.

FSI on the race to innovation: Legacy VS Technology. How to accelerate collaboration between Data Science & Data Engineering?

Webinar

During this webinar, Brian McConnell, Solution Architect at Databricks will bring technical solutions - including Databricks Runtime, Delta Lake and Mlflow, to the main challenges that financial services and insurance companies are facing nowadays. Topics that will be covered: - the regulatory triangle: Regulators, Legislators and Industry - regulation and the Data Driven Organisation - dealing with change, and lots of it - reproducibility.

Apache Spark + AI Munich Meetup – Graph July 2019

Meetup

Munich, Germany

We are delighted to announce our graph focused event this summer in Munich. This event will be hosted by b.telligent together with two great speakers from the graphs community:

  • Dr. Andreas Hopfgartner & Dr. Sebastian Petry (b.telligent)
  • Martin Junghanns (Neo4j)

Talk 1: Graphs are everywhere!? - Not yet, but maybe soon.
Talk 2: Extending Apache Spark Graph for the Enterprise with Morpheus and Neo4j